Li Huiyan

Closed-loop fuzzy control of Parkinsonian state based on slow variable

Closed-loop deep brain stimulation is an effective method for controlling the Parkinsonian state. However, the twin issues of how to obtain a suitable feedback variable and design a high-performance control strategy are still unresolved. This paper proposes a variable universe fuzzy closed-loop control method based on slow variable to modulate the abnormal Parkinsonian state. For highly nonlinear neural systems, in order to achieve energy optimization of the control signal, this paper designs a closed-loop control strategy of thalamic neurons by combining unscented Kalman filter with variable universe fuzzy control, with the objective of improving the firing patterns of thalamic neurons via external stimuli with lower energy consumption. Using a slow variable as the feedback variable significantly decreases the fluctuations and energy expenditure of the stimuli. Qualitative and quantitative analyses conducted demonstrate that the proposed variable universe fuzzy closed-loop control strategy based on slow variable is effective.

An Improved Robust ISS-satisfying Control for Synchronization of FHN Neurons

In the conventional robust ISS (input to state stable) which satisfies control strategy, all parameters of the system must be known and deterministic. Since it confines the application, in this paper an attempt is made to create a bridge between two important design techniques, i.e., the robust ISS-satisficing control strategy and the variable universe indirect fuzzy control strategy, to offset this weakness. The new control method proposed has the inverse optimality of robust ISS-satisficing control and the robust and predictive performance of fuzzy control. Due to the control Lyapunov method, the overall closed-loop system is shown to be stable. In this work, the new control strategy is used in synchronization of two FHN neurons. The simulation results are given to confirm the control algorithm is feasible and performances well.

Fire Patterns of HH Neuron under External Sinusoidal ELF Stimulus

Neuron as the main information carrier in neural systems is able to generate diverse fire trains in response to different stimuli. In this paper, the stimulus frequency is taken as the bifurcation parameter, and the ISI is considered to be one of the state variables. Via numerical simulation, we mainly concentrate on investigating the kinds of fire patterns that the HH neuron model displays such as period-n, bursting, and modulation fire patterns, etc. under the effect of external sinusoidal ELF electric field, and the relation between the ISI sequences and the external stimulus just like synchronization. In addition, an explanation is put forwards from the electrophysiology point of view to try to interpret why neurons generate so many different kinds of ISI sequences.